Latest Articles for Deep Learning

A new approach enhances segmentation in medical images without detailed labels.

New method improves image reconstruction using perspective changes.

A novel approach to enhance acoustic sensing without compromising audio quality.

EncodeNet enhances DNN accuracy without increasing model size.

Introducing UGEs, a new way to keep data safe and usable.

A novel approach improves denoising by using a trace-constraint loss function.

Exploring new methods to improve uncertainty representation in deep learning.

Learn how to enhance matrix multiplication performance for deep learning on Versal ACAP.

Counterfactual reasoning enhances understanding of vulnerabilities in code.

New model emoDARTS improves accuracy in recognizing speech emotions using deep learning.

This study explores code clones' impact on deep learning frameworks and software quality.

SynCellFactory generates synthetic cell videos to enhance training data for better tracking.

New model improves depth estimation using event camera data through efficient algorithms.

A new method enhances predictive power by converting tabular data into images.

RSTAR-Net reduces streak artifacts in lung cancer images, enhancing treatment precision.

An overview of testing methods for deep learning libraries and their significance.

Llip enhances how images are matched with diverse textual descriptions.

SGD-PH combines first-order and second-order methods for better model training performance.

Discover GRAWA, a method improving distributed training for deep learning models.

New techniques reduce memory access and boost performance in deep learning models.

A new approach to enhance deep learning models against adversarial attacks.

New techniques improve model performance against data shifts using non-convex loss functions.

Explore the rise and efficiency of Vision Transformers in image processing.

Discover a new method for efficient neural architecture search through graph-based evaluations.

Exploring specialized neurons designed for specific tasks in artificial networks.

This article discusses the security challenges of deep learning models and defense strategies.

This paper discusses a white-box model for effective unsupervised learning.

BiSHop enhances deep learning for tabular data with innovative techniques.

An overview of issues and methods in cerebrovascular segmentation for medical imaging.

A new model improves transformer performance by managing outlier inefficiency.

Exploring how SAM improves model performance amid label noise.

A framework that enhances clustering performance using self-supervised learning techniques.

A look at how AdamW improves training in deep learning models.

A new framework enhances image quality while reducing processing time and resource use.

A new measure improves divergence comparisons among multiple probability distributions.

LHU-Net improves accuracy and efficiency in medical image segmentation, aiding healthcare professionals.

This article discusses effective gradient estimators for quantization-aware training in deep learning.

New methods improve tracking skin features for health assessment applications.

DeepJSCC improves image quality in relay networks, integrating transmission with deep learning.

SuRGe improves low-resolution images using advanced GAN techniques.